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Localization Based on Social Big Data Analysis in the Vehicular Networks

机译:车网中基于社会大数据分析的本地化

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摘要

Location-based services, especially for vehicular localization, are an indispensable component of most technologies and applications related to the vehicular networks. However, because of the randomness of the vehicle movement and the complexity of a driving environment, attempts to develop an effective localization solution face certain difficulties. In this paper, an overlapping and hierarchical social clustering model (OHSC) is first designed to classify the vehicles into different social clusters by exploring the social relationship between them. By using the results of the OHSC model, we propose a social-based localization algorithm (SBL) that use location prediction to assist in global localization in the vehicular networks. The experiment results validate the performance of the OHSC model and show that the presented SBL algorithm demonstrates superior localization performance compared with the existing methods.
机译:基于位置的服务,特别是针对车辆本地化的位置服务,是与车辆网络相关的大多数技术和应用必不可少的组成部分。然而,由于车辆运动的随机性和驾驶环境的复杂性,试图开发有效的定位解决方案的尝试面临某些困难。在本文中,首先设计了一种重叠和分层的社会聚类模型(OHSC),以通过探索车辆之间的社会关系将其分类为不同的社会聚类。通过使用OHSC模型的结果,我们提出了一种基于社会的定位算法(SBL),该算法使用位置预测来辅助车辆网络中的全局定位。实验结果验证了OHSC模型的性能,并表明与现有方法相比,本文提出的SBL算法具有更好的定位性能。

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